Structural Compressed Network Coding for Data Collection in Cluster-Based Wireless Sensor Networks

作     者：Zhao, Yimin Xiao, Song Gan, Hongping Li, Lizhao Xiao, Lina 

作者机构：Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China 

出 版 物：《IEICE TRANSACTIONS ON COMMUNICATIONS》 (电子情报通信学会汇刊，英文版，B:通信)

年 卷 期：2019年第E102B卷第11期

页      面：2126-2138页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：network coding compressed sensing structure-sparse wireless sensor networks data collection 

摘      要：To efficiently collect sensor readings in cluster-based wireless sensor networks, we propose a structural compressed network coding (SCNC) scheme that jointly considers structural compressed sensing (SCS) and network coding (NC). The proposed scheme exploits the structural compressibility of sensor readings for data compression and reconstruction. Random linear network coding (RLNC) is used to re-project the measurements and thus enhance network reliability. Furthermore, we calculate the energy consumption of intra- and inter-cluster transmission and analyze the effect of the cluster size on the total transmission energy consumption. To that end, we introduce an iterative reweighed sparsity recovery algorithm to address the all-or-nothing effect of RLNC and decrease the recovery error. Experiments show that the SCNC scheme can decrease the number of measurements required for decoding and improve the network s robustness, particularly when the loss rate is high. Moreover, the proposed recovery algorithm has better reconstruction performance than several other state-of-the-art recovery algorithms.